R Notebook

Variables:

Risk
Money
Security
Good time Help Success Proper Environment Tradition Creativity

Friends important Family important Leisure time Happiness Health (subjective) Satisfaction Freedom

Sex Age Country Wave Marital status Children Employment Education

library(data.table)
library(tidyr)

#read the data (Wave 5)

# Data of Wave 5


WV5_data <- readRDS("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/F00007944-WV5_Data_R_v20180912.rds")


# Convert WV5_data-object in data.frame 
WV5_data_df <- as.data.frame(WV5_data)

# show first five columns
head(WV5_data_df[, 1:5])

clean the data set

library(dplyr)

#rename the variables
WV5_data <- WV5_data_df %>%
  rename(sex = V235, age = V237, country = V2, wave = V1, family_important = V4, friends_important = V5, leisure_time = V6, happiness = V10, health = V11, satisfaction = V22, freedom = V46, marital_status = V55, children = V56, creativity = V80, money = V81, security = V82, goodtime = V83, help = V84, success = V85, risk = V86, proper = V87, environment = V88, tradition = V89, employment = V241, education = V238)
WV5_data

#select only the variables of interest
WV5_data <- WV5_data %>%
  select(sex, age, country, wave, family_important, friends_important, leisure_time, happiness, health, satisfaction, freedom, marital_status, children, creativity, money, security, goodtime, help, success, risk, proper, environment, tradition, employment, education)
WV5_data

#decode the country names 
countrynames = read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV5_data$country_lab = countrynames$name [match(WV5_data$country, countrynames$code)]
table(WV5_data$country_lab)

            Andorra           Argentina           Australia 
               1003                1002                1421 
             Brazil            Bulgaria        Burkina Faso 
               1500                1001                1534 
             Canada               Chile               China 
               2164                1000                1991 
           Colombia          Cyprus (G)               Egypt 
               3025                1050                3051 
           Ethiopia             Finland              France 
               1500                1014                1001 
            Georgia             Germany               Ghana 
               1500                2064                1534 
      Great Britain           Guatemala           Hong Kong 
               1041                1000                1252 
            Hungary               India           Indonesia 
               1007                2001                2015 
               Iran                Iraq               Italy 
               2667                2701                1012 
              Japan              Jordan            Malaysia 
               1096                1200                1201 
               Mali              Mexico             Moldova 
               1534                1560                1046 
            Morocco         Netherlands         New Zealand 
               1200                1050                 954 
             Norway                Peru              Poland 
               1025                1500                1000 
            Romania              Russia              Rwanda 
               1776                2033                1507 
           Slovenia        South Africa         South Korea 
               1037                2988                1200 
              Spain              Sweden         Switzerland 
               1200                1003                1241 
             Taiwan            Thailand Trinidad and Tobago 
               1227                1534                1002 
             Turkey             Ukraine       United States 
               1346                1000                1249 
            Uruguay            Viet Nam              Zambia 
               1000                1495                1500 

WV5_data

NA
NA

#Read Dataset (Wave 6)

WV6_data <- load("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/WV6_Data_R_v20201117.rdata") 
WV6_data <- WV6_Data_R_v20201117 
print(WV6_data)

` ``{r} #rename variables

WV6_data <- WV6_data %>%
  rename(wave = V1, sex = V240, age = V242,country = V2, marital_status = V57, children = V58, employment = V229, education = V248, risk = V76, money = V71, security = V72, goodtime =  V73, help = V74B, success = V75, proper = V77, environment = V78, tradition = V79, creativity = V70, family_important = V4, friends_important = V5, leisure_time = V6, happiness = V10, health = V11, satisfaction = V23, freedom = V55 )


#select only the variables of interest
WV6_data <- WV6_data %>%
  select(sex, age, country, wave, marital_status, children, employment, education, risk, money, security, goodtime, help, success, proper, environment, tradition, creativity, family_important, friends_important, leisure_time, happiness, health, satisfaction, freedom)
WV6_data

NA

#decode daraset (Wave 6)

countrynames = read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV6_data$country_lab = countrynames$name [match(WV6_data$country, countrynames$code)]
table(WV6_data$country_lab)

            Algeria           Argentina             Armenia 
               1200                1030                1100 
          Australia          Azerbaijan             Belarus 
               1477                1002                1535 
             Brazil               Chile               China 
               1486                1000                2300 
           Colombia          Cyprus (G)             Ecuador 
               1512                1000                1202 
              Egypt             Estonia             Georgia 
               1523                1533                1202 
            Germany               Ghana               Haiti 
               2046                1552                1996 
          Hong Kong               India                Iraq 
               1000                4078                1200 
              Japan              Jordan          Kazakhstan 
               2443                1200                1500 
             Kuwait          Kyrgyzstan             Lebanon 
               1303                1500                1200 
              Libya            Malaysia              Mexico 
               2131                1300                2000 
            Morocco         Netherlands         New Zealand 
               1200                1902                 841 
            Nigeria            Pakistan           Palestine 
               1759                1200                1000 
               Peru         Philippines              Poland 
               1210                1200                 966 
              Qatar             Romania              Russia 
               1060                1503                2500 
             Rwanda           Singapore            Slovenia 
               1527                1972                1069 
       South Africa         South Korea               Spain 
               3531                1200                1189 
             Sweden              Taiwan            Thailand 
               1206                1238                1200 
Trinidad and Tobago             Tunisia              Turkey 
                999                1205                1605 
            Ukraine       United States             Uruguay 
               1500                2232                1000 
         Uzbekistan               Yemen            Zimbabwe 
               1500                1000                1500 

WV6_data

#combine the 2 dataset (Wave 6 + Wave 5)

WV5_data

WV6_data

data = rbind(WV5_data, WV6_data)
data

#number of countries

length(unique(data$country_lab))

[1] 80

#number of participants

nrow(data)

[1] 173540

#exclusion of participants

data = subset(data, risk > 0 & sex > 0 & age > 0 & education > 0 & employment > 0 & marital_status > 0 & children >= 0 & family_important > 0 & friends_important > 0 & leisure_time > 0 & happiness > 0 & health > 0 & satisfaction > 0 & freedom > 0 & marital_status > 0 & creativity > 0 & money > 0 & security > 0 & goodtime >0 & help > 0 & success > 0, risk > 0 & proper > 0 & environment > 0 & tradition > 0 & employment > 0 & education > 0) 

 

data

#number of males vs females (1 = males; 2 = females)

table(data$sex)

# Check the unique responses of each variable with frequencies
for (col_name in names(data)) {
  response_table <- table(data[[col_name]])
  print(paste("Response frequencies for", col_name, ":"))
  print(response_table)

#create a categorical age variable

data$sex[data$sex == 1] <- "male"
data$sex[data$sex == 2] <- "female"

#gender variables

mean(data$age)

[1] 41.59569

#average age of participants

range(data$age) 

[1] 15 99

#age range

library(ggplot2)
ggplot(data, aes(x = risk)) +
  geom_histogram(binwidth = 0.5, fill = "lightblue", color = "black") +
  labs(x = "Risk Taking", y = "Frequency", title = "Histogram of Risk Taking") +
  theme_minimal()

#risk taking Frequency

ggplot(data, aes(x = age)) +
  geom_histogram(binwidth = 0.5, fill = "lightblue", color = "black") +
  labs(x = "Age", y = "Frequency", title = "Histogram of Age Distributionn") +
  theme_minimal()

#age frequency

ggplot(data, aes(x = agecat, y = risk)) +
  geom_boxplot() +
  labs(title = "Boxplot of Risk and Adventure by Age",
       x = "Age",
       y = "Risk and Adventure") +
  theme_minimal()

NA
NA

#age vs risk taking

ggplot(data, aes(as.factor(sex), risk))+
  geom_boxplot()

#sex vs risk taking

summary(data)

     sex                 age          country           wave       family_important friends_important  leisure_time      happiness          health        satisfaction   
 Length:149626      Min.   :15.0   Min.   : 12.0   Min.   :5.000   Min.   :-5.000   Min.   :-5.000    Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000  
 Class :character   1st Qu.:28.0   1st Qu.:276.0   1st Qu.:5.000   1st Qu.: 1.000   1st Qu.: 1.000    1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 5.000  
 Mode  :character   Median :39.0   Median :484.0   Median :6.000   Median : 1.000   Median : 2.000    Median : 2.000   Median : 2.000   Median : 2.000   Median : 7.000  
                    Mean   :41.6   Mean   :481.5   Mean   :5.552   Mean   : 1.094   Mean   : 1.661    Mean   : 1.871   Mean   : 1.865   Mean   : 2.106   Mean   : 6.755  
                    3rd Qu.:53.0   3rd Qu.:710.0   3rd Qu.:6.000   3rd Qu.: 1.000   3rd Qu.: 2.000    3rd Qu.: 2.000   3rd Qu.: 2.000   3rd Qu.: 3.000   3rd Qu.: 8.000  
                    Max.   :99.0   Max.   :894.0   Max.   :6.000   Max.   : 4.000   Max.   : 4.000    Max.   : 4.000   Max.   : 4.000   Max.   : 5.000   Max.   :10.000  
                                                                   NA's   :221      NA's   :351       NA's   :698      NA's   :573      NA's   :230      NA's   :340     
    freedom       marital_status     children       creativity         money           security         goodtime           help          success            risk      
 Min.   :-5.000   Min.   :1.000   Min.   :0.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.00   Min.   :-5.000   Min.   :1.000  
 1st Qu.: 6.000   1st Qu.:1.000   1st Qu.:0.000   1st Qu.: 2.000   1st Qu.: 3.000   1st Qu.: 1.000   1st Qu.: 2.000   1st Qu.: 1.00   1st Qu.: 2.000   1st Qu.:3.000  
 Median : 7.000   Median :1.000   Median :2.000   Median : 3.000   Median : 4.000   Median : 2.000   Median : 3.000   Median : 2.00   Median : 3.000   Median :4.000  
 Mean   : 7.004   Mean   :2.715   Mean   :1.843   Mean   : 2.718   Mean   : 3.846   Mean   : 2.374   Mean   : 3.273   Mean   : 2.29   Mean   : 2.951   Mean   :3.801  
 3rd Qu.: 9.000   3rd Qu.:6.000   3rd Qu.:3.000   3rd Qu.: 4.000   3rd Qu.: 5.000   3rd Qu.: 3.000   3rd Qu.: 5.000   3rd Qu.: 3.00   3rd Qu.: 4.000   3rd Qu.:5.000  
 Max.   :10.000   Max.   :6.000   Max.   :8.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.00   Max.   : 6.000   Max.   :6.000  
 NA's   :838                                      NA's   :972      NA's   :602      NA's   :442      NA's   :566      NA's   :44862   NA's   :703                     
     proper        environment       tradition        employment      education     country_lab           agecat         
 Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :1.000   Min.   :1.000   Length:149626      Length:149626     
 1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.:1.000   1st Qu.:3.000   Class :character   Class :character  
 Median : 2.000   Median : 2.000   Median : 2.000   Median :3.000   Median :5.000   Mode  :character   Mode  :character  
 Mean   : 2.533   Mean   : 2.468   Mean   : 2.511   Mean   :3.406   Mean   :5.501                                        
 3rd Qu.: 3.000   3rd Qu.: 3.000   3rd Qu.: 3.000   3rd Qu.:5.000   3rd Qu.:7.000                                        
 Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   :8.000   Max.   :9.000                                        
 NA's   :541      NA's   :561      NA's   :518                                                                           

#data cleaning: deletion of NAs 
data = na.omit(data)
summary(data)

     sex                 age           country           wave       family_important friends_important  leisure_time   
 Length:101172      Min.   :15.00   Min.   : 12.0   Min.   :5.000   Min.   :-5.000   Min.   :-5.000    Min.   :-5.000  
 Class :character   1st Qu.:27.00   1st Qu.:268.0   1st Qu.:5.000   1st Qu.: 1.000   1st Qu.: 1.000    1st Qu.: 1.000  
 Mode  :character   Median :39.00   Median :458.0   Median :5.000   Median : 1.000   Median : 2.000    Median : 2.000  
                    Mean   :41.11   Mean   :474.4   Mean   :5.348   Mean   : 1.099   Mean   : 1.652    Mean   : 1.893  
                    3rd Qu.:53.00   3rd Qu.:710.0   3rd Qu.:6.000   3rd Qu.: 1.000   3rd Qu.: 2.000    3rd Qu.: 2.000  
                    Max.   :99.00   Max.   :894.0   Max.   :6.000   Max.   : 4.000   Max.   : 4.000    Max.   : 4.000  
   happiness          health        satisfaction       freedom      marital_status     children       creativity    
 Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.00   Min.   :1.000   Min.   :0.000   Min.   :-5.000  
 1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 5.000   1st Qu.: 5.00   1st Qu.:1.000   1st Qu.:0.000   1st Qu.: 2.000  
 Median : 2.000   Median : 2.000   Median : 7.000   Median : 7.00   Median :1.000   Median :2.000   Median : 2.000  
 Mean   : 1.889   Mean   : 2.098   Mean   : 6.692   Mean   : 6.91   Mean   :2.769   Mean   :1.835   Mean   : 2.699  
 3rd Qu.: 2.000   3rd Qu.: 3.000   3rd Qu.: 8.000   3rd Qu.: 9.00   3rd Qu.:6.000   3rd Qu.:3.000   3rd Qu.: 4.000  
 Max.   : 4.000   Max.   : 5.000   Max.   :10.000   Max.   :10.00   Max.   :6.000   Max.   :8.000   Max.   : 6.000  
     money           security         goodtime           help           success            risk           proper      
 Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :1.000   Min.   :-5.000  
 1st Qu.: 3.000   1st Qu.: 1.000   1st Qu.: 2.000   1st Qu.: 1.000   1st Qu.: 2.000   1st Qu.:3.000   1st Qu.: 1.000  
 Median : 4.000   Median : 2.000   Median : 3.000   Median : 2.000   Median : 3.000   Median :4.000   Median : 2.000  
 Mean   : 3.842   Mean   : 2.363   Mean   : 3.243   Mean   : 2.281   Mean   : 2.937   Mean   :3.827   Mean   : 2.538  
 3rd Qu.: 5.000   3rd Qu.: 3.000   3rd Qu.: 5.000   3rd Qu.: 3.000   3rd Qu.: 4.000   3rd Qu.:5.000   3rd Qu.: 3.000  
 Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   :6.000   Max.   : 6.000  
  environment       tradition       employment      education     country_lab           agecat          education_cat     
 Min.   :-5.000   Min.   :-5.00   Min.   :1.000   Min.   :1.000   Length:101172      Length:101172      Length:101172     
 1st Qu.: 2.000   1st Qu.: 1.00   1st Qu.:1.000   1st Qu.:3.000   Class :character   Class :character   Class :character  
 Median : 2.000   Median : 2.00   Median :3.000   Median :5.000   Mode  :character   Mode  :character   Mode  :character  
 Mean   : 2.452   Mean   : 2.51   Mean   :3.467   Mean   :5.309                                                           
 3rd Qu.: 3.000   3rd Qu.: 3.00   3rd Qu.:5.000   3rd Qu.:7.000                                                           
 Max.   : 6.000   Max.   : 6.00   Max.   :8.000   Max.   :9.000                                                           

#ris vs education
ggplot(data, aes(risk, education))+
  geom_point()+
  geom_smooth(method = "lm")

model = lm(risk ~ education, data = data)
summary(model)

Call:
lm(formula = risk ~ education, data = data)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0532 -1.0532  0.1564  1.2612  2.3660 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.10560    0.01183  347.08   <2e-16 ***
education   -0.05240    0.00202  -25.95   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.589 on 101170 degrees of freedom
Multiple R-squared:  0.00661,   Adjusted R-squared:  0.0066 
F-statistic: 673.1 on 1 and 101170 DF,  p-value: < 2.2e-16

ggplot(data, aes(risk, freedom))+
  geom_point()+
  geom_smooth(method = "lm")

model1 = lm(risk ~ freedom, data = data)
summary(model1)

Call:
lm(formula = risk ~ freedom, data = data)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3968 -1.1100  0.1769  1.2247  2.3204 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.157773   0.014987  277.43   <2e-16 ***
freedom     -0.047814   0.002045  -23.38   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.59 on 101170 degrees of freedom
Multiple R-squared:  0.005375,  Adjusted R-squared:  0.005365 
F-statistic: 546.7 on 1 and 101170 DF,  p-value: < 2.2e-16

ggplot(data, aes(as.factor(wave), risk))+
  geom_boxplot()

ggplot(data, aes(risk, age))+
  geom_point()+
  geom_smooth(method = "lm")

attach(data)
data$education_cat[education < 3] = "incomplete or no primary education"
data$education_cat[education > 2 & education <= 6] <- "no uni"
data$education_cat[education >= 7] <- "uni"
detach(data)
table(data$education)

    1     2     3     4     5     6     7     8     9 
 9751  9603 18657 11323 29208 10845 24715 10923 24601 

data

data$wave[data$wave == 5] <- "Wave 5"
data$sex[data$wave == 6] <- "Wave 6"
data

data$wave[data$wave == 5] <- "Wave 5"
data$sex[data$wave == 6] <- "Wave 6"
data

```